Retrieval-Augmented Generation (RAG) Projects

Large language models are powerful - but they don't know anything about your data. Retrieval-Augmented Generation (RAG) changes that. Instead of expensive fine-tuning or stuffing entire documents into every prompt, RAG retrieves exactly the right information at query time and hands it to the LLM as context. The result: AI applications that are accurate, grounded, and actually deployable. What You'll Build: Project 1: A PDF question-answering chatbot with conversational memory and page-level source citation Project 2: A multi-document research assistant that synthesizes information across dozens of files and detects when sources conflict Project 3: A code documentation assistant that uses AST-based chunking to understand your codebase and auto-generate docstrings Project 4: A customer support bot with confidence scoring, dynamic knowledge base updates, and smart escalation to human agents Project 5: A hybrid search system that combines semantic embeddings with BM25 keyword search using Reciprocal Rank Fusion - so exact matches and product codes are never missed Project 6: A production-ready RAG pipeline with semantic caching, rate limiting, async request handling, Docker containerization, CI/CD, and Prometheus monitoring Beyond the Projects: The final section covers systematic evaluation using the RAGAS framework - so you can measure faithfulness, answer relevancy, and context precision instead of guessing whether your system works. You'll also learn re-ranking with cross-encoders, query expansion, and a decision tree for diagnosing and fixing retrieval quality problems. Who This Book Is For: This book is written for Python developers with some exposure to AI concepts who want to build real RAG applications - not toy tutorials. You don't need a machine learning background. You do need to be comfortable with Python and ready to ship something.